How To Use PlotBins() When Working From A Combined SQM Object

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Introduction

When working with SqueezeMeta, a powerful tool for analyzing metagenomic data, combining multiple SQM objects can be a useful approach for comparing different samples. However, when using the combineSQMlite() function, the resulting SQM object may not be directly compatible with certain functions, such as plotBins(). In this article, we will explore how to use plotBins() when working from a combined SQM object.

Understanding the SQM Object

Before diving into the solution, it's essential to understand the structure of an SQM object. An SQM object is a list containing various entries, including 'misc', 'taxa', and 'functions'. When combining two SQM objects using combineSQMlite(), the resulting object is a list with the same entries, but the data is merged from both samples.

The Challenge with plotBins()

The plotBins() function is used to visualize the number of reads that mapped to bins versus those that did not. However, when working with a combined SQM object, this function may not work as expected. The issue lies in the fact that the plotBins() function is designed to work with individual SQM objects, not combined ones.

Solution: Extracting the Required Data

To overcome this challenge, we need to extract the required data from the combined SQM object. We can do this by accessing the 'misc' entry of the object, which contains the bin information. Specifically, we need to extract the 'bin' and 'reads' columns from the 'misc' entry.

# Load the required libraries
library(SqueezeMeta)

# Combine two SQM objects using combineSQMlite()
combined_sqm <- combineSQMlite(sample1, sample2)

# Extract the 'misc' entry from the combined SQM object
misc_entry <- combined_sqm$misc

# Extract the 'bin' and 'reads' columns from the 'misc' entry
bin_data <- misc_entry[, c("bin", "reads")]

Using plotBins() with the Extracted Data

Now that we have extracted the required data, we can use the plotBins() function to visualize the number of reads that mapped to bins versus those that did not. We can pass the extracted data to the plotBins() function using the data argument.

# Use plotBins() with the extracted data
plotBins(data = bin_data)

Customizing the Plot

The plotBins() function provides various options for customizing the plot. For example, we can change the title of the plot using the main argument.

# Customize the plot title
plotBins(data = bin_data, main = "Reads Mapped to Bins vs Not")

Conclusion

In this article, we have demonstrated how to use plotBins() when working from a combined SQM object. By extracting the required data from the combined SQM object and passing it to the plotBins() function, we can visualize the number of reads that mapped to bins versus those that did not. This approach provides a useful way to compare different samples and gain insights into the metagenomic data.

Additional Tips and Variations

  • To customize the plot further, we can use various options provided by the plotBins() function, such as changing the colors, adding labels, or modifying the axis limits.
  • We can also use other visualization tools, such as ggplot2, to create more complex and customized plots.
  • To compare multiple samples, we can repeat the process of extracting the required data and using plotBins() for each sample, and then combine the resulting plots using tools like grid.arrange().

Example Use Case

Suppose we have two SQM objects, sample1 and sample2, and we want to compare the number of reads that mapped to bins versus those that did not. We can combine the two SQM objects using combineSQMlite() and then extract the required data using the steps outlined above. Finally, we can use plotBins() to visualize the results.

# Combine two SQM objects using combineSQMlite()
combined_sqm <- combineSQMlite(sample1, sample2)

# Extract the required data
misc_entry <- combined_sqm$misc
bin_data <- misc_entry[, c("bin", "reads")]

# Use plotBins() to visualize the results
plotBins(data = bin_data)

Frequently Asked Questions

In this article, we will address some of the most common questions related to using plotBins() with a combined SQM object.

Q: What is the difference between a combined SQM object and an individual SQM object?

A: A combined SQM object is a list containing the merged data from multiple SQM objects, whereas an individual SQM object is a list containing the data from a single sample.

Q: Why can't I use plotBins() directly with a combined SQM object?

A: The plotBins() function is designed to work with individual SQM objects, not combined ones. When you combine multiple SQM objects, the resulting object is a list with the same entries, but the data is merged from both samples. This can cause issues with the plotBins() function, which expects a single SQM object.

Q: How do I extract the required data from a combined SQM object?

A: To extract the required data, you need to access the 'misc' entry of the combined SQM object, which contains the bin information. Specifically, you need to extract the 'bin' and 'reads' columns from the 'misc' entry.

Q: What if I want to compare multiple samples?

A: To compare multiple samples, you can repeat the process of extracting the required data and using plotBins() for each sample, and then combine the resulting plots using tools like grid.arrange().

Q: Can I customize the plot using plotBins()?

A: Yes, you can customize the plot using various options provided by the plotBins() function, such as changing the colors, adding labels, or modifying the axis limits.

Q: What if I want to use a different visualization tool, such as ggplot2?

A: You can use other visualization tools, such as ggplot2, to create more complex and customized plots. However, you will need to extract the required data and then use the visualization tool to create the plot.

Q: Are there any additional tips or variations for using plotBins() with a combined SQM object?

A: Yes, here are some additional tips and variations:

  • To customize the plot further, you can use various options provided by the plotBins() function, such as changing the colors, adding labels, or modifying the axis limits.
  • You can also use other visualization tools, such as ggplot2, to create more complex and customized plots.
  • To compare multiple samples, you can repeat the process of extracting the required data and using plotBins() for each sample, and then combine the resulting plots using tools like grid.arrange().

Example Use Cases

Here are some example use cases for using plotBins() with a combined SQM object:

  • Suppose you have two SQM objects, sample1 and sample2, and you want to compare the number of reads that mapped to bins versus those that did not. You can combine the two SQM objects using combineSQMlite() and then extract the required data using the steps outlined above. Finally, you can use plotBins() to visualize the results.
  • Suppose you have multiple SQM objects and you want to compare the number of reads that mapped to bins versus those that did not across all samples. You can repeat the process of extracting the required data and using plotBins() for each sample, and then combine the resulting plots using tools like grid.arrange().

By following these steps and tips, you can effectively use plotBins() with a combined SQM object and gain valuable insights into the metagenomic data.